Time to First Token (TTFT)
Time to First Token (TTFT) is an efficiency metric that measures the duration from when a request is sent to an LLM to when the first token of the response is generated. When data transmission time is minimal, TTFT primarily reflects the time required for prefilling the context and predicting the initial token.
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Ch.5 Inference - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Prep Sessions
Edge-Native Mixture-of-Experts Serving with FreeToken @ University of Michigan - Ann Arbor
Ch.4 Platform Adaptation and Performance Evaluation - Edge-Native Mixture-of-Experts Serving with FreeToken @ University of Michigan - Ann Arbor
Agentic Workload Serving and Cross-Hardware Performance - Edge-Native Mixture-of-Experts Serving with FreeToken @ University of Michigan - Ann Arbor
Related
Request Latency
Throughput
Time to First Token (TTFT)
Inter-token Latency (ITL)
Tokens Per Second (TPS)
Resource Utilization in LLM Inference
Energy Efficiency in LLM Inference
Cost Efficiency in LLM Inference
A startup is building a real-time, interactive chatbot to help customers troubleshoot technical issues. Their engineering team evaluates two different language models, 'Model X' and 'Model Y'. The team's final report concludes that Model X is superior because its responses are consistently more accurate and helpful across a wide range of test queries. Based on this report, the company decides to deploy Model X. Which of the following statements identifies the most critical potential weakness in
LLM Selection for a High-Volume Chatbot
A team is evaluating a large language model for deployment. Match each evaluation goal below to the primary category of metric it represents: 'Output Quality' or 'Efficiency'.
You are evaluating two candidate long-context LLMs...
You lead evaluation for an internal eDiscovery ass...
Your team is writing an internal evaluation checkl...
Your team is selecting an LLM for an internal "pol...
Selecting a Long-Context LLM for a Cost-Constrained Enterprise Document Assistant
Choosing Long-Context Evaluation Evidence for a High-Volume Contract Review Feature
Designing an Evaluation Plan for a Long-Context Compliance Copilot Under Latency and Cost Constraints
Reconciling Long-Context Retrieval Quality with Inference Efficiency for a Meeting-Transcript Copilot
Evaluating a Long-Context LLM for Audit-Ready Evidence Retrieval Under Throughput Constraints
Diagnosing Conflicting Long-Context Evaluation Signals for an Internal Knowledge Assistant
Decode Throughput Stability in Agentic Serving
Tail Time-to-First-Token as an Agentic Availability Boundary
Cross-Hardware MoE Serving across Memory and Interconnect Tiers
Pipelined Full-Layer Double Buffering in MoE Prefill
Time to First Token (TTFT)
Tokens Per Second (TPS)
Throughput
Learn After
A company is developing a conversational AI for a customer service chatbot. User testing reveals that customers perceive the chatbot as 'slow' or 'unresponsive' primarily due to the noticeable pause between them sending a message and the chatbot starting to type its reply. To directly address this specific user perception issue, which efficiency metric should the engineering team focus on minimizing?
A user reports that a chatbot application feels very responsive because it begins generating its answer almost instantly. Based on this observation alone, it is valid to conclude that the underlying language model is also highly efficient at generating long, multi-paragraph responses.
When data transmission latency is minimal, what two computational operations primarily account for the duration required to generate an LLM's first response token?
Tail Time-to-First-Token as an Agentic Availability Boundary
Identify the efficiency metric being measured in these tests, and explain the primary computational operation responsible for the increased delay observed in Test B before response generation began.